The Speak team is Spotifies in-house text-to-speech (TTS) team, supporting products like DJ, AI Voice Translation, as well as the development of exciting new unreleased products. We focus on building world class speech technologies that can power the next generation of personalized generative voice products at scale.
What You'll Do
Build large-scale speech and audio data pipelines using frameworks like Google Cloud Platform and Apache Beam Work on machine learning projects powering new generatie AI experiences and helping to build state-of-the-art text-to-speech models Learn and contribute to the teams understanding of best practicies and techniques for building data pipelines for large scale generative models, including cleaning, filtering, classifying and labelling Collaborate with other engineers, researchers, product managers and stakeholders, taking on learning and leadership opportunities that arise Deliver scalable, testable, maintainable, and high-quality code. Share knowledge, promote standard methodologies, making your team the best version of itself through mentorship and constructive accountability
Who You Are
You have Data Engineering experience and you know how to work with high-volume, heterogeneous data, preferably with distributed systems such as Hadoop, BigTable, Cassandra, GCP, AWS or Azure You have experience with one or more higher-level Python or Java based data processing frameworks such as Beam, Dataflow, Crunch, Scalding, Storm, Spark, Flink etc. You have strong python programming abilities Experience using pre-trained ML models is a plus You might have worked with Docker as well as Luigi, Airflow, or similar tools You care about quality and you know what it means to ship high quality code You have experience managing data retention policies You care about agile software processes, data-driven development, reliability, and responsible experimentation You understand the value of collaboration and partnership within teams.
Were You'll Be
This role is located in London, UK or Stockholm, Sweden
